CN110059423A - Tropical cyclone objective strength determination method based on multi-factor generalized linear model - Google Patents
Tropical cyclone objective strength determination method based on multi-factor generalized linear model Download PDFInfo
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Abstract
The invention provides a tropical cyclone objective strength determining method based on a multi-factor generalized linear model, which comprises the following steps of: step 1, extracting characteristic factors of the tropical cyclone at each moment, wherein the characteristic factors comprise MMV, RD, S-20, TBBstd, Lat or Lon strength factors; and 2, establishing a relation between the characteristic factors and the maximum surface wind speed of the tropical cyclone by using a generalized linear model. The invention can further improve the accuracy of objectively strengthening the tropical cyclone and provides technical support for monitoring and early warning of the tropical cyclone.
Description
Technical field
The present invention relates to tropical cyclone intensity monitoring and warning technical fields, are based on multiple-factor broad sense in particular to one kind
The tropical cyclone of linear model is objective to determine strong method.
Background technique
Tropical cyclone is the strong cyclonic circulation occurred on low latitudes ocean surface, is often accompanied by blast, heavy rain, storm tide
Equal diastrous weathers, seriously threaten the safety of life and property of the mankind.World Meteorological Organization's statistics shows tropical cyclone in recent years
It has become and causes damages maximum natural calamity to human society, at the same time, China is the whole world by tropical cyclones influence and calamity
Evil one of the countries with the most serious ....
Nearly ten years, the course guidance ability of tropical cyclone is significantly improved, however from the tropical cyclone of same period
From the point of view of forecast of intensity result, but fluctuates always nearly ten years or even forecast precision error is in the trend increased year by year.This
Outside, the subjective intensity forecast overall performance based on statistical method still leads over the objective forecast knot based on numerical model
Fruit.Therefore, the fixed of tropical cyclone is still current insoluble major issue by force.
It is currently based on the most widely used method of satellite cloud picture analysis tropical cyclone, is Davorak by 1970s
Itd is proposed Davorak technology and the various modifieds of subsequent continuous development.However these methods belong to subjective analysis method, need
The analysis that forecaster is continuous and intensive is wanted, and accuracy depends critically upon the forecast experience and forecast level of analyst.
For the limitation of previous subjective analysis method,It proposes and a kind of is obtained using infrared cloud picture of satellite
The shape of TC cloud cluster structure and the objective method of dynamic characteristic, i.e. drift angle variance technique (Deviation Angle
VarianceTechnique:DAV-T).When developing in the never organized cloud cluster of tropical cyclone system and reinforcing, cloud cluster knot
Structure becomes more axial symmetry relative to a special reference point.This method can be by calculating on satellite infrared cloud atlas
Bright temperature gradient obtains the symmetrization degree of each tropical cyclone system, and with this systematism level amount of progress to tropical cyclone
Change.The result shows that this method is equal during the entire life cycle of the nascent phase of TC system, period of expansion, maturity period and phase of withering away
It is objective effective.However, previous still has improvement empty based on the fixed strong DAV-T of single-factor in the fixed strong precision of tropical cyclone
Between.
Summary of the invention
The invention introduces a variety of confactors on the basis of DAV-T, can significantly improve determining for tropical cyclone
Strong precision.
Goal of the invention:
It is an object of that present invention to provide one kind based on multiple-factor generalized linear (Generalized linear model:
GLM) tropical cyclone of model is objective determines strong method, provides reference for the objective fixed strong process of tropical cyclone.
Inventive technique scheme:
A kind of tropical cyclone based on multiple-factor generalized linear model is objective to determine strong method, including the following steps:
Step 1 is based on satellite high resolution observations data and history tropical cyclone, extracts tropical cyclone intensity when each
The characterization factor at quarter；Characterization factor includes:
MMV: the drift angle variance minimum of tropical cyclone；
RD: the relative distance between boiling pot and its drift angle variance yields minimum；
S-20: the bright temperature of cloud cluster is lower than -20 DEG C of areas within the scope of 50~200km of tropical cyclone circulation center；
TBBstd: the bright temperature standard deviation of cloud cluster within the scope of 100~300km of tropical cyclone circulation center；
Lat: latitude where boiling pot；
Lon: longitude where boiling pot；
Step 2 is based on generalized linear model, establishes between the characterization factor and tropical cyclone maximum surface wind speed Vmax
Relationship, steps are as follows:
(201) for known m group training data (Vi,X1,X2,Xp,...Xn), p=1,2 ..., n.,
Wherein: ViFor the i-th moment tropical cyclone sea surface maximum wind velocity；(X1,X2,Xp,...Xn)T, p=1,2 ..., n.
For ViCorresponding each characterization factor；
The interaction two-by-two of each characterization factor and characterization factor is standardized first, is obtained based on broad sense
The predictive factor item of linear model:
Wherein, the single-factor of predictive factor acts on item:
Interaction item two-by-two between predictive factor:
Characterization factor and Vmax relationship based on generalized linear model are as follows:
Wherein:For the V obtained by generalized linear modelmaxMatch value；b0For constant coefficient；(x1,x2,x3,...xn)TGeneration
Table is to the fixed strong each predictive factor of tropical cyclone；Item, b are acted on for the single-factor of predictive factoriFor its coefficient；Interaction item two-by-two between predictive factor, biFor its coefficient；ε is residual error, and it is 0 that residual error, which meets mean value,
Normal distribution；
B can be obtained by formula 30And biOr bij；
(202) characterization factor in need of test is substituted into the predicted value in formula 3 to get tropical cyclone intensity.
Preferably, the value of coefficient is as follows in formula 3:
The utility model has the advantages that
Tropical cyclone based on multiple-factor generalized linear model of the invention is objective to determine strong method, can further increase pair
The objective fixed strong precision of tropical cyclone, provides technical support for the monitoring and warning of tropical cyclone.
Detailed description of the invention
Fig. 1 is the flow chart of the intensity of typhoon monitoring method based on MMV fitting.
Fig. 2 is fixed strong based on single-factor sigmoid function and multiple-factor GLM model under different tropical cyclone intensity grades
Deviation profile compares figure.Left figure is the deviation profile that Sigmoid model determines different tropical cyclone intensity grades strong result；Right figure
For generalized linear model different tropical cyclone intensity grades are determined with the deviation profile of strong result.Wherein, A, B, C, D respectively represent G-
2015, tetra- verifying collection of G-2016, G-2017 and G-all.
Specific embodiment
In order to better understand the technical content of the present invention, special to lift specific embodiment and institute's accompanying drawings is cooperated to be described as follows.
With reference to the accompanying drawings, a kind of tropical cyclone based on multiple-factor generalized linear model proposed by the present invention is objective determines Qiang Fang
Method further increases fixed strong precision objective to tropical cyclone, provides technical support for the monitoring and warning of tropical cyclone.
The present invention is based on DAV-T methods and generalized linear model to realize.
Process as shown in connection with fig. 1, the tropical cyclone based on multiple-factor generalized linear model is objective to be determined strong method and includes:
Step 1 extracts tropical cyclone in the characterization factor at each moment, includes:
(1) MMV: the drift angle variance minimum (unit: deg of tropical cyclone2)；
(2) RD: the relative distance (unit: km) between boiling pot and its drift angle variance yields minimum；
(3) S-20: lower than -20 DEG C areas of the bright temperature of cloud cluster are (single within the scope of 50~200km of tropical cyclone circulation center
Position: pixel)；
(4) TBBstd: within the scope of 100~300km of tropical cyclone circulation center the bright temperature standard deviation of cloud cluster (unit:
℃)；
(5) Lat: latitude where boiling pot；
(6) Lon: longitude where boiling pot；
These characterization factors determine Qiang Yinzi as what this determined strong method.The fixed strong factor further include the features described above factor two-by-two it
Between interaction.
Step 2 establishes characterization factor and tropical cyclone maximum surface wind speed (V using generalized linear modelmax) between pass
System；
(1) for known m group training data (Vi,X1,X2,Xp,...Xn), p=1,2 ..., n., wherein ViFor difference
Moment tropical cyclone sea surface maximum wind velocity, (X1,X2,Xp,...Xn)T, p=1,2 ..., n. is corresponding each to determine Qiang Yinzi.
First to it is each determine Qiang Yinzi and its interaction item be standardized:
Wherein, single-factor item:
Interact item two-by-two:
The form of expression of generalized linear model is as follows:
Wherein,For the V obtained by modelmaxMatch value, (x1,x2,x3,...xn)TIt represents fixed to tropical cyclone strong
Each predictive factor, b1, b2..., bnThe coefficient of Ge Yin subitem is represented,It is acted on for the single-factor of each predictive factor
,Interaction item two-by-two between predictive factor, ε are that (it is 0 that default meets mean value to residual error
Normal distribution).
Using model above formula, each coefficient b because of subitem is obtained1, b2..., bn.
(2) Qiang Yinzi (x is determined by need of test1,x2,xp,...xn), p=1,2 ..., n. is updated to step (1)
In obtained model, the tropical cyclone intensity of estimation can be obtained.
Wherein, the value of model coefficient is as follows:
In order to verify the present invention fixed strong effect objective for tropical cyclone, by model of the present invention and Pineros etc.
(2015) the single-factor Sigmoid established determines strong model and is compared.Northwest Pacific area institute between choosing 2015~2017 years
There is the tropical cyclone example of generation as research sample.Each year data information is successively selected as test set, and by remaining year
The data information of part assesses the fixed potent fruit of model as training set.Additionally the data in all times are made simultaneously
For training set and test set, potent fruit is determined to investigate model to the entirety of all data.Data group name and it is classified as follows table 1:
Table 1
It is affected in view of DAV calculates radius to the fixed strong result of single-factor Sigmoid model, while to GLM model
Middle partial factors have an impact, and therefore, will calculate every 5 lattice in 25 lattice point of radius (about 250km) to 55 lattice points (about 550km) range
Two class models at point interval determine the root mean square that strong result obtains afterwards compared with China Meteorological Administration's optimal path (CMA-BST) data set
Error (RMS) is compared, as a result such as the following table 2:
Table 2
By upper table analysis it is found that for all calculating radiuses, the root-mean-square error of multiple-factor GLM model compares single-factor
Sigmoid model shows to be greatly lowered in all test data sets, hence it is evident that improves surely potent fruit.
Fig. 2 is shown under different tropical cyclone intensity grades based on single-factor sigmoid function and multiple-factor GLM model
Fixed strong deviation profile compares.From the fixed strong result of Sigmoid model, the fixed strong deviation of B group verifying collection is relatively relatively low, and
C group verifying collection is then relatively higher.When tropical cyclone intensity is lower than TS grade, the extremum of fixed strong deviation will be far more than other
Period, maximum determine strong absolute value of the bias close to 60m/s.And for generalized linear model, it can be seen that tropical cyclone is from < TD to TS
In the change procedure in intensity stage, the degree of scatter of fixed strong deviation is more stable.Although deviation extremum number of cases mesh is also more
Concentrate on tropical cyclone intensity weaker period, with the enhancing of TC intensity, the median of fixed strong deviation from overgauge gradually to
Minus deviation variation, the process have significant continuity Characteristics.And generally speaking, most of sample example is determined at strong error
In -20~20ms-1.Compared with Sigmoid model, the distribution that generalized linear model determines strong error is more concentrated, whole magnitude
It is smaller and more unified for the result between different verifying collection.
Although the present invention has been disclosed as a preferred embodiment, however, it is not to limit the invention.Skill belonging to the present invention
Has usually intellectual in art field, without departing from the spirit and scope of the present invention, when can be used for a variety of modifications and variations.Cause
This, the scope of protection of the present invention is defined by those of the claims.
Claims (2)
1. a kind of tropical cyclone based on multiple-factor generalized linear model is objective to determine strong method, which is characterized in that including following step
It is rapid:
Step 1 is based on satellite high resolution observations data and history tropical cyclone, extracts tropical cyclone intensity at each moment
Characterization factor；Characterization factor includes:
MMV: the drift angle variance minimum of tropical cyclone；
RD: the relative distance between boiling pot and its drift angle variance yields minimum；
S-20: the bright temperature of cloud cluster is lower than -20 DEG C of areas within the scope of 50~200km of tropical cyclone circulation center；
TBBstd: the bright temperature standard deviation of cloud cluster within the scope of 100~300km of tropical cyclone circulation center；
Lat: latitude where boiling pot；
Lon: longitude where boiling pot；
Step 2 is based on generalized linear model, establishes the pass between the characterization factor and tropical cyclone maximum surface wind speed Vmax
System, steps are as follows:
(201) for known m group training data (Vi,X1,X2,Xp,...Xn), p=1,2 ..., n.,
Wherein: ViFor the i-th moment tropical cyclone sea surface maximum wind velocity；(X1,X2,Xp,...Xn)T, p=1,2 ..., n. ViIt is right
The each characterization factor answered；
The interaction two-by-two of each characterization factor and characterization factor is standardized first, is obtained based on generalized linear
The predictive factor item of model:
Wherein, the single-factor of predictive factor acts on item:
Interaction item two-by-two between predictive factor:
Characterization factor and V based on generalized linear modelmaxRelationship are as follows:
Wherein:For the V obtained by generalized linear modelmaxMatch value；b0For constant coefficient；(x1,x2,x3,...xn)TRepresentative pair
The fixed strong each predictive factor of tropical cyclone；Item, b are acted on for the single-factor of predictive factoriFor its coefficient；
Interaction item two-by-two between predictive factor, biFor its coefficient；ε is residual error, and residual error meets the normal distribution that mean value is 0；
B can be obtained by formula 30And biOr bij；
(202) characterization factor in need of test is substituted into the predicted value in formula 3 to get tropical cyclone intensity.
2. the tropical cyclone according to claim 1 based on multiple-factor generalized linear model is objective to determine strong method, feature
It is, in formula 3, the value of coefficient is as follows:
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111695473A (en) * | 2020-06-03 | 2020-09-22 | 中国人民解放军国防科技大学 | Tropical cyclone strength objective monitoring method based on long-time and short-time memory network model |
| CN116360013A (en) * | 2023-04-04 | 2023-06-30 | 中国气象局上海台风研究所(上海市气象科学研究所) | Typhoon objective strength determination method and system with gradient wind balance |
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| US8160995B1 (en) * | 2008-04-18 | 2012-04-17 | Wsi, Corporation | Tropical cyclone prediction system and method |
| CN107230197A (en) * | 2017-05-27 | 2017-10-03 | 浙江师范大学 | Tropical cyclone based on satellite cloud picture and RVM is objective to determine strong method |
| CN107229825A (en) * | 2017-05-23 | 2017-10-03 | 浙江大学 | A kind of tropical cyclone complete trails analogy method assessed towards calamity source |
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| US8160995B1 (en) * | 2008-04-18 | 2012-04-17 | Wsi, Corporation | Tropical cyclone prediction system and method |
| CN107229825A (en) * | 2017-05-23 | 2017-10-03 | 浙江大学 | A kind of tropical cyclone complete trails analogy method assessed towards calamity source |
| CN107230197A (en) * | 2017-05-27 | 2017-10-03 | 浙江师范大学 | Tropical cyclone based on satellite cloud picture and RVM is objective to determine strong method |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111695473A (en) * | 2020-06-03 | 2020-09-22 | 中国人民解放军国防科技大学 | Tropical cyclone strength objective monitoring method based on long-time and short-time memory network model |
| CN111695473B (en) * | 2020-06-03 | 2023-12-19 | 中国人民解放军国防科技大学 | Tropical cyclone strength objective monitoring method based on long-short-term memory network model |
| CN116360013A (en) * | 2023-04-04 | 2023-06-30 | 中国气象局上海台风研究所(上海市气象科学研究所) | Typhoon objective strength determination method and system with gradient wind balance |
| CN116360013B (en) * | 2023-04-04 | 2023-10-10 | 中国气象局上海台风研究所(上海市气象科学研究所) | Typhoon objective strength determination method and system with gradient wind balance |
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